Computer-implemented methods of determining protein viscosity

ABSTRACT

Provided herein are high-throughput methods for identifying a candidate antibody based on viscosity of the candiate antibody.

RELATED APPLICATIONS

This application is a national stage filing under 35 U.S.C. § 371 ofInternational Application No. PCT/US2014/038368, filed May 16, 2014,which was published under PCT Article 21(2) in English, and which claimsthe benefit under 35 U.S.C. § 119(e) of U.S. Provisional ApplicationSer. No. 61/824,680, filed on May 17, 2013, the entire contents of eachof which are incorporated herein by reference.

FIELD OF THE INVENTION

Aspects of the invention relate to computer-implemented methods forpredicting viscosities of compositions comprising proteins (such asantibodies) and screening proteins based on the predicted viscosities.

BACKGROUND OF INVENTION

Many therapeutic proteins such as, for example, antibodies may beadministered via subcutaneous injection. This injection pathway requiresa high protein concentration in the final solution to be injected (Shireet al., J Pharm Science, 2004; 93(6): 1390-1402; Roskos et al., DrugDevel Res, 2004; 61(3): 108-120). Achieving the high proteinconcentration necessary for subcutaneous delivery can be problematic.

SUMMARY OF INVENTION

Some embodiments of techniques described herein providecomputer-implemented methods for automatically and quantitativelypredicting the viscosity of a solution comprising a protein based oninformation regarding the protein, such as information regarding thestructure or other properties of the protein. Information regarding theprotein may be input, for example, in the form of a computer-generatedstructure of the protein, such as a predicted structure of the proteinfollowing folding. Analysis of that computer-generated structure orinformation regarding the computer-generated structure may bequantitatively analyzed using the techniques described herein toidentify anticipated structural properties of the protein and, based onthose properties, form a prediction of the viscosity of a particularsolution comprising that protein. In some embodiments, techniques arealso provided for screening proteins (such as, but not limited to,antibodies) based on their viscosities in solution, providing aconvenient, rapid and inexpensive way to identify those proteins morelikely to be of interest as candidates. As used herein, a candidate is aprotein (such as an antibody) typically selected from a plurality ofproteins based on a particular characteristic (e.g., viscosity) and thatmay be used and/or further developed. Candidates include clinicalcandidates, intending candidates that will be used and/or furtherdeveloped for use in vivo.

Viscosity of antibodies has been previously studied, but the inventorshave recognized and appreciated limitations of this prior work. Theprior approaches were limited to determining an electrostatic potentialof a protein's surface and identifying, based on that potential, whetherthe studied antibodies would be highly viscous. Visual inspection ofindividual antibodies by a human user was a necessary part of thatanalysis. The inventors recognized and appreciated that this approachwas cumbersome and slow to use, at least due to the required visualinspection of each antibody. Worse, the inventors recognized andappreciated that the evaluation of electrostatic potential wasqualitative and that the electrostatic potential scales used in thisprevious work limited the accuracy of the approach to no-better-thanaverage.

In contrast, the computer-implemented methods provided herein arequantitative. As laid out below, whereas the prior techniquesexclusively relied on human assessments of an antibody's electrostaticpotential to determine viscosity, a viscosity prediction facility asdescribed herein, executing on one or more computing devices, mayanalyze computer-generated information on a predicted structure of aprotein (e.g., a structure following folding of the protein) to identifyseveral anticipated structural properties of that protein. The viscosityprediction facility may then, in accordance with some of the techniquesdescribed herein, evaluate those anticipated structural properties andperform calculations on numeric values regarding those anticipatedproperties to produce a numeric value. Such calculations may, in someembodiments, additionally be performed using information regarding adesired solution including the protein. The numeric value produced usingtechniques described herein correlates with viscosity of the protein, orof the solution including the protein, that was analyzed. In someembodiments, therefore, the viscosity prediction facility mayadditionally evaluate the numeric value to form a prediction of theviscosity of that protein/solution.

Accordingly, described herein are computer-implemented techniques that,for each protein, (i) perform calculations based on evaluations ofnumeric values indicative of anticipated properties of the protein and(ii) report a numeric result that can be used to classify a protein aspotentially having low or high viscosity. The computer-implementedtechniques do not require visual inspection or qualitative analysis by ahuman user and, since the techniques described herein do not use theelectrostatic scale used in the prior visualization techniques, theaccuracy can be improved beyond that previously offered. Furthermore, asthe approaches are completely quantitative, they facilitatehigh-throughput analysis. Lastly, since SCM is quantitative, it iseasily extended to multiple structures of the protein obtained usingmolecular dynamic simulation.

In some embodiments, a viscosity prediction facility may perform ananalysis referred to herein as a spatial charge map (SCM). SCM is acomputational, predictive, high-throughput analysis that aids in thediscovery and development of proteins. In some embodiments, the proteinsare antibodies including monoclonal antibodies (mAbs). SCM allows forrapid in silico screening of proteins based on their viscosities. Asused herein, viscosity of a protein refers to viscosity of a compositionsuch as a solution comprising such protein. The SCM analysis can beperformed during the early stages of a development process and can leadto identification of proteins with desirable viscosity profiles. Thisknowledge can be used to prioritize protein candidates for furtherdevelopment.

Various aspects and embodiments of the invention are described in termsof antibodies and more specifically monoclonal antibodies. However it isto be understood that the invention embraces proteins generally, andthat the recitations relating to antibodies are to be understood asexemplary of the broader class of proteins.

SCM is described in detail below. Briefly, SCM involves as a first stepobtaining a three-dimensional or tertiary structure(s) of the protein ofinterest, such as an antibody. This may be done experimentally or it maybe done in silico by converting the primary amino acid sequence of aprotein (or a protein fragment or domain, such as an antibody fragmentor antibody domain) to a three dimensional or tertiary structure(s).This may be done, for example, using homology modeling. The structure isthen analyzed using SCM. SCM relies, in part, on negative charges orregions of negative charge on the solvent accessible surface ofproteins. SCM can be analyzed under one or more conditions orenvironments such as pH, and a single SCM value will be output for eachcondition. As an example, a SCM score may be determined for a protein atpH of greater than or equal to 7 and at a pH less than or equal to about5.5, with one SCM value being output for each pH. The value of SCM Scorecorrelates with the viscosity of the protein (or a solution comprisingthe protein, as used interchangeably herein).

Thus, in one aspect, the invention provides a method of determiningviscosity of a protein, the method comprising, analyzing arepresentation of a structure of the protein or a portion of the proteinto compute a score for the protein (or portion thereof) based on aparameter of each atom in the structure under a condition, anddetermining viscosity of the protein based on the computed score. Insome embodiments, the protein is an antibody such as a monoclonalantibody.

In another aspect, the invention provides a computer-implemented methodof determining viscosity of a protein such as an antibody, the methodcomprising, by at least one processor, analyzing a representation of atleast one structure of at least a portion of the protein to compute ascore for the at least a portion of the protein based on at least oneparameter of each atom in the at least one structure under at least onecondition, and determining the viscosity of the protein based on thecomputed score. In some embodiments, the protein is an antibody such asa monoclonal antibody.

In another aspect, the invention provides a method of identifying one ormore candidates from a plurality of proteins. The candidate proteins maybe clinical candidates to be used or to be developed further. The methodcomprises generating at least one molecular structure of at least aportion of an protein from the plurality of proteins, analyzing theleast one molecular structure to compute a score for the at least aportion of the protein based on a partial charge and solvent exposure ofeach atom in the at least one molecular structure under at least onecondition, predicting viscosity of the protein based on the computedscore, and identifying the protein as a candidate of interest based onthe predicted viscosity of the protein. In some embodiments, the proteinis an antibody such as a monoclonal antibody.

In yet another aspect, the invention provides a method of screening aplurality of proteins to identify candidates for use or development, themethod comprising computing a score for at least a portion of a proteinfrom the plurality of proteins based on partial charges and solventexposures of atoms in at least one molecular structure of the at least aportion of a protein, wherein the computed score is correlated withviscosity of the protein; and selecting the protein as a proteincandidate based on the computed score. In some embodiments, the proteinis an antibody such as a monoclonal antibody.

These and other aspects and embodiments of the invention will bedescribed in greater detail herein.

BRIEF DESCRIPTION OF FIGURES

FIG. 1 is a plot of SCM prediction versus experimentally obtainedviscosity. The x-axis is the experimentally measured viscosity of sixdifferent IgG1 mAbs (mAb concentration=150 mg/ml, 20 mM Histidine, 220mM Sucrose, 0.04% PS-20, pH 5.5).

FIG. 2 is a comparison of SCM predictions at two different pH values formAb10 with experimentally measured viscosity of mAb10. Experiments wereperformed using 100 mg/ml of mAb10 in 10 mM His buffer in presence of200 mM Trehalose and 0.03 mM Tween 80. Experimental values are in thefirst bar of each pair. SCM predicted values are in the second bar ofeach pair.

FIG. 3 is a comparison of SCM predictions at two different pH values formAb6 with experimentally measured viscosity of mAb6. Experiments wereperformed using 150 mg/ml of mAb6 in 20 mM His buffer. Experimentalvalues are in the first bar of each pair. SCM predicted values are inthe second bar of each pair.

FIG. 4 is a ranking of various IgG1 mAbs for their viscosities at twodifferent pH values using the SCM tool. The first bar for each antibodyindicates the SCM prediction at pH ˜7 while the second bar indicates SCMprediction at pH ˜5.5. The antibodies left to right are mAb1, mAb2,mAb3, mAb4, mAb5, mAb6, mAb7, mAb8, mAb9, Erbitux, Rituxan, Avastin,Herceptin, and CNTO607.

FIGS. 5A and B are illustrations of atomic SCM values projected onto theFab domain of CNTO607. The regions indicated in red are patches ofexposed negative residues and hence antibody variants in which thesenegative residues are mutated to either neutral or positively-chargedresidues should display low viscosities. FIG. 5A is a color rendering.FIG. 5B is a gray scale rendering. The color denotes atomic SCM valueswith −2 values being in red and +2 values being in blue.

FIG. 6 is a flowchart illustrating generally a process of screeningantibodies based on their viscosities, in accordance with someembodiments of the invention. It is to be understood that the inventioncontemplates generalization of the process to proteins.

FIG. 7 is a flowchart illustrating a process of determining viscosity ofan antibody using a structure of the antibody, in accordance with someembodiments of the invention. It is to be understood that the inventioncontemplates generalization of the process to proteins. Such a processmay be represented or referred to as an algorithm.

FIG. 8 is a flowchart illustrating a process of determining viscosity ofan antibody using multiple structures of the antibody, in accordancewith some embodiments of the invention. It is to be understood that theinvention contemplates generalization of the process to proteins. Such aprocess may be represented or referred to as an algorithm.

FIG. 9 is a block diagram of an exemplary computing environment on whichsome embodiments of the invention may be implemented.

DETAILED DESCRIPTION OF INVENTION

Compositions containing proteins (e.g., monoclonal antibodies) are, inmany instances, injected or infused via subcutaneous injection.Subcutaneous injection, in many instances, conveniently can be performedoutside of a clinical setting and without a medical practitioner'sassistance. However, viscoelastic resistance to hydraulic conductance inthe subcutaneous tissue, backpressure generated upon injection, andperceptions of pain all limit subcutaneous injection volumes to smallvolumes sometimes on the order of approximately 2 ml. Therefore, proteincompositions intended for subcutaneous injection usually are highlyconcentrated and thus also highly viscous. It is this latter propertythat further limits their utility.

Described herein are computer-based utilities, referred to as viscosityprediction facilities, for analyzing computer-generated informationregarding a protein of interest (e.g., an antibody), inferringanticipated structural properties of the protein, and performingcomputations on information regarding the anticipated structuralproperties. The computations described herein and performed by thefacilities may yield numeric values that correlate with viscosity. Suchcomputations may, in some cases, be additionally performed usinginformation on a desired solution including the protein, to yieldinformation on predicted viscosity in the desired solution. Thefacilities may also analyze the numeric value output from thecomputations to make a prediction of a viscosity of theprotein/solution.

A viscosity prediction facility may be implemented using any of varioustechniques described herein. Spatial charge map (SCM) is one techniquedescribed herein for analyzing a computer-generated informationregarding an anticipated structure of a protein to yield information ona predicted viscosity. Below, a viscosity prediction facilityimplementing SCM is referred to as an SCM tool. Viscosity predictionfacilities (including SCM tools) described herein may execute on one ormore computing devices to evaluate information regarding protein(s)and/or solution(s) and identify candidates based on predictionsregarding viscosities.

For convenience, viscosity prediction techniques are described below interms of analyzing antibodies, but it is to be understood that thesetechniques may be applied to other proteins as well. The proteins may beclinical candidates although they are not so limited.

An SCM tool may be implemented as a structure-based phenomenologicalmolecular-modeling tool, which identifies patches of charged residues onthe protein surface. In embodiments, the input to an SCM tool is astructure of the protein such as an antibody. If the structure is notavailable experimentally, homology-modeling software can be used tomodel the structure of the protein using its sequence. It is to beunderstood that the structure modeling can be done using either an aminoacid sequence or a nucleic acid sequence. The following describesvarious steps that may be carried out by an SCM tool, in the context ofantibodies, to form a prediction of viscosity based oncomputer-generated information regarding the structure of an antibody.The steps may be performed on non-antibody proteins also.

As a first step, the SCM tool assigns each atom in the antibody apartial charge. The SCM tool can obtain the partial charge in a varietyof ways, including using software like PropKa or directly borrowed fromforce fields. In the exemplifications provided herein, partial chargeson each protein atom are borrowed using CHARMM27 force field. Since thepartial charges on atoms are typically dependent on the formulationconditions including the formulation pH, the SCM calculation ispreferably performed at various pH values if the dependence of viscosityon formulation pH is needed.

Based on the protein structure and the atomic partial charges at theformulation pH, the SCM tool computes an SCM value of each atom usingthe following equation if a single structure of the protein isavailable:

$\begin{matrix}{{SCM}_{{atom},i} = {\sum\limits_{{{{side} - {{chain}\mspace{14mu}{atoms}}},{{which}\mspace{14mu}{belong}\mspace{14mu}{to}\mspace{14mu}{an}}}{{exposed}\mspace{20mu}{residue}\mspace{14mu}{and}}{{are}\mspace{14mu}{within}\mspace{14mu}{distance}\mspace{14mu} R}{{of}\mspace{14mu}{the}\mspace{14mu}{atom}\mspace{14mu} i}}\;{{partial}\mspace{14mu}{charge}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{atom}}}} & (1)\end{matrix}$

In Eq. 1, the SCM tool considers a residue to be an exposed residue ifthe total solvent accessible area (computed using water with proberadius of 1.4 Å) of all the side-chain atoms of the residue in theprotein structure is greater than a particular cut-off. The cut-off maybe any cut-off in and between 1 to 50 Å². In some embodiments, thecut-off may be 10 Å². In Eq. 1, the value of distance ‘R’ is greaterthan zero. In the various exemplifications provided herein, an SCM toolmay compute SCM values at R=5 and 10 Å. In general, an SCM tool maycompute an SCM score at any value of R, which is greater than zero. Rvalues ranging from 5-20 Å can be used with the understanding the lowerR values provide higher resolution but more noise and conversely thehigher R values provide lower resolution but less noise.

In the context of antibodies, once the SCM value for each of the atomsis computed, the SCM tool computes a variety of SCM scores for variousdomains of the antibody as:

$\begin{matrix}{{{Fab}\mspace{14mu}{SCM}\mspace{14mu}{Score}} = {\sum\limits_{{{all}\mspace{14mu}{atoms}\mspace{14mu}{in}}{{the}\mspace{14mu}{Fab}\mspace{14mu}{domain}}}\;{SCM}_{{atom},i}}} & (2) \\{{{Fab}\mspace{14mu}{positive}\mspace{14mu}{SCM}\mspace{14mu}{Score}} = {\sum\limits_{{{all}\mspace{14mu}{atoms}\mspace{14mu}{in}}{{the}\mspace{14mu}{Fab}\mspace{14mu}{domain}}}\;{{SCM}_{{atom},i} \times {H\left( {SCM}_{{atom},i} \right)}}}} & (3) \\{{{Fab}\mspace{14mu}{negative}\mspace{14mu}{SCM}\mspace{14mu}{Score}} = {\sum\limits_{{{all}\mspace{14mu}{atoms}\mspace{14mu}{in}}{{the}\mspace{14mu}{Fab}\mspace{14mu}{domain}}}\;{{SCM}_{{atom},i} \times {H\left( {- {SCM}_{{atom},i}} \right)}}}} & (4) \\{{{Fv}\mspace{14mu}{SCM}\mspace{14mu}{Score}} = {\sum\limits_{{{all}\mspace{14mu}{atoms}\mspace{14mu}{in}}{{the}\mspace{14mu}{Fv}\mspace{14mu}{domain}}}\;{SCM}_{{atom},i}}} & (5) \\{{{Fv}\mspace{14mu}{positive}\mspace{14mu}{SCM}\mspace{14mu}{Score}} = {\sum\limits_{{{all}\mspace{14mu}{atoms}\mspace{14mu}{in}}{{the}\mspace{14mu}{Fv}\mspace{14mu}{domain}}}\;{{SCM}_{{atom},i} \times {H\left( {SCM}_{{atom},i} \right)}}}} & (6) \\{{{Fv}\mspace{14mu}{negative}\mspace{14mu}{SCM}\mspace{14mu}{Score}} = {\sum\limits_{{{all}\mspace{14mu}{atoms}\mspace{14mu}{in}}{{the}\mspace{14mu}{Fv}\mspace{14mu}{domain}}}\;{{SCM}_{{atom},i} \times {H\left( {- {SCM}_{{atom},i}} \right)}}}} & (7)\end{matrix}$where H(x) is the Heaviside function (i.e., H(x)=1 for x≥0, H(x)=0 forx<0). The SCM tool can similarly compute corresponding SCM scores of theCDR.

If multiple structures or conformations of the antibody are available,an SCM tool may compute the averages and the standard deviations of theabove-mentioned SCM scores (Eqns. 2-7) after computing the atomic SCMvalues and the SCM scores for each structure or conformation of theantibody.

The steps an SCM tool, executing on one or more computing devices, maytake to compute the SCM score of an antibody are described below.

Step 1. Structure of the Fab Domain:

Obtain the structure of the Fab domain of the antibody. If the structureof the Fab domain is not available from experiments, the structure canbe modeled using a variety of available software like WAM, PEGS,Rosetta, Accelrys, MOE, Schrodinger, etc. Some of these softwarepackages generate coordinates for heavy atoms and hydrogen atoms of aprotein. Examples include Accelrys, MOE and Schrodinger. Others generatecoordinates for only heavy atoms. Examples include WAM, PEGS andRosetta. If the latter class of package is used, a subsequent step isperformed to generate the coordinates of the hydrogen atoms.

Step 2. Partial Charge of Each Atom at the Formulation pH:

A variety of available software packages like PropKa, Accelrys,Schrodinger, etc. can be used to deduce the partial charge on each atomof the Fab domain at the formulation pH. In the simulations providedherein, partial charges are assigned to the atoms using the CHARMM27force field. At a pH>pKa of Histidine, it is assumed that all Histidineresidues have neutral side chains while at a pH<pKa of Histidine, it isassumed that all Histidine residues have positively charged side chains.The pKa of Histidine is about 6.2-6.5. Other methods of assigningprotonation states to Histidine side chain can also be employed.

Step 3. Compute SAA of Each Residue in the Protein:

For each residue, the total solvent accessible area (SAA) of all itsside-chain atoms is computed using the structure of the protein.

Step 4: Identify Exposed Residues:

All residues with SAA>10 Å² are classified as exposed residues (i.e.,surface exposed residues) and residues with SAA<10 Å² are classified asburied residues. A different threshold for this area cutoff can also beused.

Step 5. Compute Atomic SCM Values:

For every atom ‘i’ in the protein, all protein atoms are identifiedwhich (1) are within distance R of this atom ‘i’ and (2) belong to theside-chain of an exposed residue. The atomic SCM value of atom ‘i’ ofthe protein is then the sum of partial charges of all these atoms. Anyvalue of R>0 can be used; in the simulations provided herein, R=10 Å isused.

Step 6. SCM Score:

Eqns. 3-7 are used to compute various SCM Scores of the various domains.

Step 7. Multiple Conformations:

If there are multiple structures or conformations of the Fab domain,steps 3-6 are repeated, thereby obtaining the average and the standarddeviation of various SCM Scores over these multiple structures orconformations.

Step 8. SCM Projection:

The SCM tool also allows the visualization of the atomic SCM valuesprojected on the protein surface. Each atom of the protein can becolored according to its SCM value. For example, an atom with a SCMvalue>0 can be colored in blue and an atom with a SCM value<0 can becolored in red.

In one example, the SCM tool may use the absolute value of ‘Fv negativeSCM Score’ computed at R=10 Å as the SCM prediction. For each mAb, theSCM tool may perform a 20 ns molecular dynamics simulation and mayextract 100 conformations from the last 10 ns of the simulation at aninterval of 100 ps. The SCM tool may also compute average and thestandard deviations in the absolute value of ‘Fv negative SCM Score.’ Ahigh absolute value of the ‘Fv negative SCM Score’ is correlated with ahigh viscosity of the mAb.

In some instances, the SCM tool may use a cutoff between low and highviscosity of about 900.

In some instances, the SCM tool may not use a strict cutoff and rathermay present (e.g., display) SCM scores for a variety of antibodies to anend user. The end user may select antibodies of interest by comparingSCMs of the variety of antibodies. Thus, viscosity may be considered tobe relative viscosity in some instances of the invention.

The SCM tool can identify candidates with high viscosity in ahigh-throughput fashion early in the discovery phase. This would allowhighly viscous mAb candidates to be excluded early in the developmentphase. In one study, experimentally measured viscosities of six IgG1mAbs under similar conditions were used to validate the SCM-basedmethod. As shown in FIG. 1, there was good correlation between theviscosity of the mAb and its absolute Fv negative SCM Score.

In general, there is a lack of available experimental data onviscosities of mAbs measured under similar conditions. Since viscosityof mAb is highly dependent on mAb concentration, formulation conditions(e.g., excipients, buffer, pH, etc.), and the like, the availableexperimental data for different mAbs measured under dissimilarconditions cannot be compared quantitatively to the SCM predictions. InFIG. 2, a qualitative validation is performed of the SCM predictionsagainst the experimental data measured under dissimilar conditions.

Table 1 provides results of a qualitative validation of the SCMpredictions against the viscosities measured under dissimilar conditions(e.g., different formulations, excipients, pH ˜5.5, mAbconcentration>100 mg/ml) for various IgG1 mAbs. SCM predictions are madeat pH ˜5.5. Viscosity of CNTO607 is reported in ref. 4 and the structureof CNTO607 is reported in ref. 5. mAb1, mAb2, mAb4, and Rituximab haveviscosity lower than 10 mPa-s while mAbS-mAb8, and CNTO607 haveviscosity higher than 10 mPa-s. Excipients like Glycine and Argininelead to viscosity reduction of antibody formulations. As shown in Table1, a high value of the absolute Fv negative SCM Score is a goodindicator of the high viscosity of the mAb.

TABLE 1 A qualitative validation of the SCM predictions against theviscosities measured under dissimilar conditions Experimental ViscosityIgG1 mAb SCM Prediction [mPa-s] Notes mAb1 1002 ± 95  8.5 mAb2 1020 ±76  8.7 mAb4  965 ± 142 6.1 Rituxan 836 ± 59 10 mAb5 1183 ± 135 20 60 mMGlycine mAb6 1513 ± 174 22 mAb7 1476 ± 177 11 51 mM Arginine mAb8 1291 ±117 >50 CNTO607 1773 ± 73  High

The SCM-based method can rank the viscosity of different mAbs as afunction of formulation pH.

Since the SCM-based method uses atomic charges for each atom at theformulation pH, it can be used to predict the dependence of IgG1viscosity on its formulation pH. In FIGS. 2 and 3, we demonstrate thatthe SCM-based method is able to correctly predict the increase inviscosity of mAb10 and mAb6 with increasing pH.

Furthermore, the SCM-based method can be used to perform theviscosity-ranking of antibodies at different formulation pH as shown inFIG. 4. FIG. 4 indicates that while the viscosity of mAb1 and mAb2 willbe comparable at pH ˜5.5, mAb2 will exhibit slightly higher viscositythan mAb1 at pH ˜7. Similarly, the SCM-based method predicts that whileat pH ˜5.5, mAb6 and mAb7 will exhibit similar viscosities, mAb6 will bemore viscous at pH ˜7.

The SCM-based method also can be used to engineer antibodies in order toproduce antibodies with lower viscosities. The SCM-based method can beused to identify patches of exposed charged residues present on theprotein surface. Based on observations on a number of IgG1 antibodies,it was predicted that patches of exposed negative residues on the Fvregion (as indicated by the high value of absolute Fv negative SCMScore) are responsible for high viscosity of these IgG1. A similarapproach can be taken for other proteins as well.

FIG. 5 illustrates atomic SCM values projected onto the Fab domain ofCNTO607. The regions indicated in red in FIG. 5 are patches of exposednegative residues and hence antibody variants in which these negativeresidues are mutated to either neutral or positively-charged residuesshould display low viscosities.

In some embodiments, an SCM tool, executing on one or more computingdevices, may be used to compute a value/score for an antibody that maythen be used to predict viscosity of the antibody. “Score,” as usedherein, refers to a score computed using the SCM tool and is thereforeinterchangeably referred to as a SCM score. The SCM tool may analyze arepresentation of one or more tertiary structures of the antibody or adomain of the antibody and compute the SCM score based on a parameter ofeach atom in the structure, including by analyzing that parameter undera condition. In some embodiments, the parameter may be a partial chargeof the atom and the condition may be a pH value, and the SCM tool maycompute a score for the antibody at a pH value. The SCM tool may thenuse the computed SCM score to predict the viscosity of the antibody andidentify whether the antibody may be a candidate of interest (e.g., fora candidate suitable for subcutaneous administration).

FIG. 6 illustrates generally a process 100 of screening antibodies basedon their viscosities, in accordance with some embodiments of theinvention. At block 102, the SCM tool receives as input a structure ofan antibody, which may have been experimentally determined or may havebeen predicted (e.g., using homology modeling or other techniques) froma sequence of the antibody. The SCM tool may receive the input in anysuitable manner, including by receiving the input from another processexecuting on a same computing device as the SCM tool or by reading theinput from one or more storage media. Next, at block 104, the SCM toolanalyzes that structure to identify regions of charged exposed residuesin the structure. The SCM tool may compute a score for the antibodybased on the identified regions, at block 106 and predict a viscosity ofthe antibody based on the computed SCM score, as shown at block 108. Forexample, an increase in an absolute value of the SCM score may becorrelated with an increase with the viscosity of the antibody. At block110, the SCM tool may identify, based on the viscosity, whether theantibody may be selected as a candidate for further development. Forexample, in some embodiments, if the viscosity of the antibody isdetermined to be low, the antibody or a fragment of the antibody may beselected as a candidate suitable for subcutaneous delivery.

FIG. 7 illustrates a process 200 of determining viscosity of an antibodyusing the SCM tool in accordance with some embodiments of the invention.The tool, according to embodiments of the invention, may be implementedin any suitable way. In some embodiments, the tool may be implemented ina computer system including computer-executable instructions that areexecuted to compute SCM scores using antibody structures. In someembodiments, the tool may be implemented to be interactive.

The process 200 may start with the SCM tool obtaining, at block 202, asequence of the antibody. The sequence may be a nucleotide sequenceencoding the antibody or an amino acid sequence. The sequence of theantibody may be previously generated sequence which may be obtained froma suitable database. Further, in some embodiments, the antibody may beextracted from a biological sample and sequenced using any suitabletechniques. The SCM tool may receive the sequence in any suitablemanner, including by receiving the sequence from another processexecuting on a same computing device as the SCM tool or by reading thesequence from one or more storage media.

Regardless of the way in which the antibody sequence is obtained by theSCM tool, next, at block 204, the SCM tool may determine a structure ofthe antibody. Alternatively, in block 204 the SCM tool may determine astructure of one or more of portions of the antibody. “Portion,” as usedherein, refers to a fragment or a domain of the antibody. For thepurpose of brevity, the following description refers to the structure ofthe antibody. However, it should be appreciated that a structure of oneor more fragments or domains of the antibody may be analyzed usingprocess 200 as described below.

The structure of the antibody may be a three-dimensional or tertiarystructure determined using X-ray crystallography, nuclear magneticresonance (NMR) spectroscopy, or using any other techniques, asembodiments of the invention are not limited in this respect.

In some embodiments, the structure of the antibody may be determinedfrom the sequence using homology modeling. Any suitable homologymodeling technique may be utilized to obtain a model of the structure ofthe antibody using its sequence. For example, software tools such asWAM, PEGS, Rosetta, Accelrys, MOE, Schrodinger, etc. may be used topredict a structure of an antibody and generate a three-dimensionalmodel of the antibody.

In embodiments in which these techniques (e.g., crystallography,spectroscopy, homology modeling) or others are used, the SCM tool maycommunicate the sequence obtained in block 202 to one or more hardwareand/or software tools that perform these techniques. The tools may be apart of the same computing device(s) as the one(s) on which the SCM toolis executing. For example, the tools may be a peripheral deviceconnected to the same computing device(s) on which the SCM tool isexecuting, or may be software executing on those same computingdevice(s). In such embodiments, the SCM tool may use any suitable inter-or intracomputer communication techniques to provide the tool(s) withthe sequence obtained in block 202 and to receive information regardingthe structure(s) resulting from the analysis performed by the tools. Anysuitable information regarding the structure(s), in any suitable format,that is indicative of the structural properties discussed below may bereceived by the SCM tool, as embodiments are not limited in thisrespect.

It should be appreciated that, while the process 200 of FIG. 2 includesthe SCM tool receiving the sequence and determining the structure inblocks 202-204, in some embodiments the SCM tool may instead receiveinformation regarding the structure of a protein as input.

In some embodiments, more than one structure of the antibody may bedetermined and analyzed, as described in more detail in connection withFIG. 8.

Because partial charges of a protein molecule depend on a solution pH,the described techniques may be applied to each of the structures at apH value. Thus, next, at block 206 in FIG. 6, the SCM tool may determinea partial charge of each atom in the structure of the antibody or itsdomain at a certain condition, such as a pH value. For example, the SCMtool may determine a SCM score for an antibody at pH of greater than orequal to 7, a pH less than or equal to about 5.5, or any other pH value.In some embodiments, partial charge of each atom in the structure of theantibody or its domain may be determined at more than one pH value, withone SCM score being output for each pH.

Any suitable techniques may be used to determine a partial charge ofeach atom in the structure of the antibody. For example, software toolssuch as PropKa, Accelrys, Schrodinger, etc. may be used to determine thepartial charge on each atom in the structure of the antibody at a pHvalue. In some embodiments, the partial charges are assigned to theatoms using CHARMM27 force field.

At block 208, for each residue in the antibody, the SCM tool may computea value representing a solvent accessible area (SAA) of side-chain atomsof the residue. The tool may compute the SAA using any suitabletechniques. For example, in some embodiments, the SAA may be computed byanalysis of the structure of the antibody using known techniques, with a“probe radius” of 1.4 Å, which approximates the radius of a watermolecule. It should be appreciated, however, that any other suitabletechniques may be utilized to determine an SAA.

At block 210, the SCM tool may identify exposed residues in thestructure based on the computed values of the SAA of the residues. Theresidues may be defined as exposed based on a threshold, which may bedetermined in any suitable manner. For example, in some embodiments, allresidues with SAA>10 Å² may be identified as exposed residues. In suchinstances, residues with SAA<10 Å² may be identified as buried residues.Though, it should be appreciated that any other thresholds may be usedincluding those in the range of 1-50 Å², as embodiments of the inventionare not limited in this respect.

At block 212, for each atom in the structure of the antibody, the SCMtool may identify a plurality of other atoms that are within a distanceR from that atom and belong to a side chain of a residue identified tobe exposed at block 210. A score for the atom, such as an SCM score, maythen be computed by the SCM tool based on the sum of partial charges ofall the other atoms. The SCM tool may compute the SCM score at any valueof R which is greater than zero. In some embodiments, R=10 Å may beutilized, though this number is provided by way of example only. Inother embodiments, R=5 Å may be used. R values ranging from 5-20 Å canbe used, wherein the lower R values provide higher resolution but morenoise and the higher R values provide lower resolution but less noise.Though, it should be appreciated that embodiments of the invention arenot limited to any particular value of the distance R, and other Rvalues may be substituted.

Further, at block 214, the SCM tool may compute an SCM score for eachatom based on partial charges of each of the atoms identified at block212. The SCM tool may compute the SCM score according the Equation (1),in which a residue is taken as an exposed residue if the total SAA ofall the side-chain atoms of this residue in the antibody structure isgreater than 10 Å². As stated herein, the cut-off may range from 1-50Å².

After the SCM tool computes SCM scores for all atoms in the structure,the SCM tool may compute a SCM score for the antibody or any domain ofthe antibody by combining the SCM scores for the atoms. For example, thescores may be computed for the Fab and Fv domains of the antibody usingthe Equations (2)-(7) shown above.

Next, at block 218, the SCM tool may predict the viscosity of theantibody based on the SCM score computed at block 216. Thus, the SCMscore computed for the antibody may be correlated with the viscosity ofthe antibody. For this purpose, a cutoff or threshold may be used todetermine whether the viscosity of the antibody is high or low. Forexample, an absolute value of the score computed for the antibody may becorrelated with the viscosity of the antibody. If the absolute value ofthe score is above the threshold, the viscosity of the antibody may bedetermined to be high. Conversely, if the absolute value of the score isless than the threshold, the viscosity of the antibody may be determinedto be low. In some embodiments, more than one threshold or cutoff forthe score may be utilized and different levels of viscosities of theantibodies may be determined.

Additionally or alternatively, in some embodiments, the viscosity of theantibody may be predicted based on the SCM score by comparing the SCMscore to SCM scores of a variety of other antibodies. In suchembodiments, viscosity of an antibody may therefore be consideredrelative viscosity.

After the SCM tool predicts the viscosity of the antibody as shown atblock 218 in FIG. 7, process 200 may end. The predicted viscosity may beused in a number of ways. For example, in some embodiments, thepredicted viscosity of the antibody may be used to determine whether theantibody may be selected as a candidate for use or further development.For example, if the viscosity of the antibody is determined to be low,the antibody may be selected as a candidate suitable for formulation forparenteral (e.g., subcutaneous) administration. The predicted viscositymay also be used to engineer antibodies to generate antibodies with lowviscosities, and for any other suitable purpose.

As discussed above, different SCM scores may be computed at differentconditions, such as different pH values. Thus, if another score isdesired to be computed for the antibody at a different condition, suchas a different pH value, process 200 may return (as shown by an arrow219 in FIG. 7) to block 206 for the SCM tool to determine partial chargeof each atom in the analyzed structure at another pH value. It should beappreciated that the order of processing at blocks 206-218 is shown inFIG. 7 by way of example only. The processing may be performed at anyother suitable order. For example, the SCM tool may determine thepartial charge of each atom in the analyzed structure, at block 206, atmore than one condition. In such scenarios, process 200 may follow toanalyze the structures at the conditions at blocks 208-218simultaneously, so that more than one score is computed for the antibodyat block 216, one for each condition. In some embodiments, the SCM toolmay compute two SCM scores, each for a different pH value (e.g., at pHof greater than or equal to 7 and at a pH less than or equal to about5.5), to predict viscosity of an antibody. Though, it should beappreciated that viscosity of an antibody may be predicted using anynumber of SCM scores computed for an antibody or its fragment using thedescribed techniques, as embodiments of the invention are not limited inthis respect.

It should be appreciated that process 200 may execute continuously, andmultiple antibodies may be screened based on their viscosities. Thus, asschematically shown by arrow 221 in FIG. 7, process 200 may return toblock 202 where the SCM tool may obtain and analyze a sequence ofanother antibody. In this way, the SCM tool may screen a large number ofantibodies as potential candidates. The candidates identified using thedescribed techniques may then be further analyzed.

In some embodiments, more than one structure, or conformation, of anantibody may be determined, and the SCM tool may compute an SCM scorefor each of the structures. The multiple structures may be predicted,for example, using molecular dynamic simulation techniques. Any numberof the structures may be determined for an antibody, including 2, 3, 4,5, 6, 7, 8, 9, 10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80,80-90, 90-100 or more.

In such embodiments, the SCM tool may compute an SCM score for anantibody based on the scores computed for each of the multiplestructures. For example, the SCM tool may compute a score for theantibody as an average of the scores computed for the multiplestructures. It should be appreciated, however, that the scores computedfor the multiple structures may be combined in any suitable way tocompute the SCM score for the antibody. Any other suitable parameters(e.g., a standard deviation of the scores for the multiple structures)may be computed for the antibody.

FIG. 8 illustrates a process 300 that an SCM tool may implement fordetermining viscosity of an antibody using multiple structures of theantibody, in accordance with some embodiments of the invention.Processing at block 202 in FIG. 8 may be similar to obtaining a sequenceof the antibody at block 202 in FIG. 7. At block 304 in FIG. 8, the SCMtool may determine a structure of the antibody (e.g., generatedexperimentally or predicted using suitable techniques), similarly toprocessing at block 204 in FIG. 7. The SCM tool may then at block 306analyze a structure of the antibody determined at block 304 to compute ascore, such as an SCM score, for the structure. The processing at block306 in FIG. 8 may be similar to processing at blocks 206-216 in FIG. 7,and is therefore not described herein in detail.

After the SCM tool computes the SCM score for the structure of theantibody, the tool may determine, at decision block 308, whether thereare more structures of the antibody to be analyzed. If the tooldetermines, at block 308, that there are more structures of the antibodyto be analyzed, process 300 may return to block 304 where anotherstructure of the antibody may be generated and analyzed by the tool atblock 306. In this way, the SCM tool may generate multiple SCM scoresfor an antibody.

If it is determined, at block 308, that there are no further structuresof the antibody to be analyzed, process 300 may follow to block 310,where the SCM tool may compute an SCM score for the antibody based onmultiple scores computed for different structures. The tool may thenpredict a viscosity of the antibody based on the computed SCM score, atblock 312.

In some embodiments, the SCM tool may present the computed SCM scores ona display in a suitable manner. For example, the SCM tool may map atomicSCM values on a representation of a surface of the antibody, and maycolor each atom of the protein according to its SCM value. For example,the SCM tool may represent an atom with a SCM value>0 using one color,and represent an atom with a SCM value<0 using a different color.Furthermore, the SCM tool may present the scores using various types ofcharts, plots, tables, diagrams and any other visual representationformats. Though, it should be appreciated that the scores computed usingthe SCM tool may be presented in any suitable manner.

In some embodiments, at least some processing steps performed by the SCMtool may be implemented as computer-readable instructions stored on oneor more non-transitory computer-readable storage media which, whenexecuted by one or more processors, cause a computing device to executethe steps. An exemplary implementation of a computer system 900 in whichsome embodiments of the invention may be implemented is shown in FIG. 9.The computer system 900 may include one or more processors 910 and oneor more computer-readable non-transitory storage media (e.g., memory 920and one or more non-volatile storage media 930). The processor 910 maycontrol writing data to and reading data from the memory 920 and thenon-volatile storage device 930 in any suitable manner, as the aspectsof the present invention described herein are not limited in thisrespect. To perform any of the functionality described herein, theprocessor 910 may execute one or more computer-executable instructionsstored in one or more computer-readable storage media (e.g., the memory920), which may serve as non-transitory computer-readable storage mediastoring instructions for execution by the processor 910. It should beappreciated that the computer system 900 may include any other suitablecomponents.

The above-described embodiments of the present invention may beimplemented in any of numerous ways. For example, the embodiments may beimplemented using hardware, software or a combination thereof. Whenimplemented in software, the software code can be executed on anysuitable processor or collection of processors, whether provided in asingle computer or distributed among multiple computers. It should beappreciated that any component or collection of components that performthe functions described above can be generically considered as one ormore controllers that control the above-discussed functions. The one ormore controllers can be implemented in numerous ways, such as withdedicated hardware, or with general purpose hardware (e.g., one or moreprocessors) that is programmed using microcode or software to performthe functions recited above.

In this respect, it should be appreciated that one implementation of theembodiments of the present invention comprises at least onenon-transitory computer-readable storage medium (e.g., a computermemory, a floppy disk, a compact disk, a tape, etc.) encoded with acomputer program (i.e., a plurality of instructions), which, whenexecuted on a processor, performs the above-discussed functions of theembodiments of the present invention. The computer-readable storagemedium can be transportable such that the program stored thereon can beloaded onto any computer resource to implement the aspects of thepresent invention discussed herein. In addition, it should beappreciated that the reference to a computer program which, whenexecuted, performs the above-discussed functions, is not limited to anapplication program running on a host computer. Rather, the termcomputer program is used herein in a generic sense to reference any typeof computer code (e.g., software or microcode) that can be employed toprogram a processor to implement the above-discussed aspects of thepresent invention.

Proteins

The methods of the invention may be used to screen proteins. Proteinsthat may be screened are typically those intended for use in vivo as forexample a therapeutic or a diagnostic. Such proteins may be used aswhole proteins or as fragments thereof including domains thereof.Examples of proteins include but are not limited to antibodies(described in greater detail below), hormones, cytokines such asinterleukins, growth factors (including those that may be used tostimulate growth of cells in vivo including for example G-CSF), enzymes(including those that may be used in enzyme replacement therapy), andthe like.

Antibodies

As used herein, the term “antibody” refers to a whole antibody. Anantibody is a glycoprotein comprising at least two heavy (H) chains andtwo light (L) chains inter-connected by disulfide bonds. Each heavychain is comprised of a heavy chain variable region (abbreviated hereinas V_(H)) and a heavy chain constant region. The heavy chain constantregion is comprised of three subdomains, C_(H1), C_(H2) and C_(H3). Eachlight chain is comprised of a light chain variable region (abbreviatedherein as V_(L)) and a light chain constant region. The light chainconstant region is comprised of one subdomain, C_(L). The V_(H) andV_(L) regions can be further subdivided into regions ofhypervariability, termed complementarity determining regions (CDR),interspersed with regions that are more conserved, termed frameworkregions (FR). Each V_(H) and V_(L) is composed of three CDRs and fourFRs arranged from amino-terminus to carboxy-terminus in the followingorder: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4. The variable regions of theheavy and light chains contain a binding domain that interacts with anantigen. The constant regions of the antibodies may mediate the bindingof the immunoglobulin to host tissues or factors, including variouscells of the immune system (e.g., effector cells) and the firstcomponent (Clq) of the classical complement system.

Antibodies fragments may be described in terms of proteolytic fragmentsincluding without limitation Fv, Fab, Fab′ and F(ab′)₂ fragments. Suchfragments may be prepared by standard methods (see, e.g., Coligan et al.Current Protocols in Immunology, John Wiley & Sons, 1991-1997,incorporated herein by reference). An antibody may comprise at leastthree proteolytic fragments (i.e., fragments produced by cleavage withpapain): two Fab fragments, each containing a light chain domain and aheavy chain domain (designated herein as a “Fab heavy chain domain”) andone Fc fragment containing two Fc domains. Each light chain domaincontains a V_(L) and a C_(L) subdomain, each Fab heavy chain domaincontains a V_(H) and a C_(H1) subdomain, and each Fc domain contains aC_(H2) and C_(H3) subdomain.

As used herein, the term “monoclonal antibody” refers to an antibodyobtained from a single clonal population of immunoglobulins that bind tothe same epitope of an antigen. Monoclonal antibodies have the same Iggene rearrangement and thus demonstrate identical binding specificity.Methods for preparing monoclonal antibodies are known in the art.

As used herein, “humanized monoclonal antibody” may refer to monoclonalantibodies having at least human constant regions and an antigen-bindingregion, such as one, two or three CDRs, from a non-human species.Humanized antibodies specifically recognize antigens of interest, butwill not evoke an immune response in humans against the antibody itself.

As used herein, the term “chimeric antibody” refers to a monoclonalantibody comprising a variable region from one source (e.g., species)and at least a portion of a constant region derived from a differentsource. In some embodiments, the chimeric antibodies comprise a murinevariable region and a human constant region.

REFERENCES

-   1. Yadav, S., Shire, S. J. & Kalonia, D. S. Viscosity Behavior of    High-Concentration Monoclonal Antibody Solutions: Correlation with    Interaction Parameter and Electroviscous Effects. Journal of    pharmaceutical sciences 101, 998-1011 (2012).-   2. Yadav, S., Shire, S. J. & Kalonia, D. S. Viscosity analysis of    high concentration bovine serum albumin aqueous solutions.    Pharmaceutical research 28, 1973-83 (2011).-   3. Yadav, S., Laue, T. M., Kalonia, D. S., Singh, S. N. &    Shire, S. J. The influence of charge distribution on    self-association and viscosity behavior of monoclonal antibody    solutions. Molecular pharmaceutics 9, 791-802 (2012).-   4. Bethea, D. et al. Mechanisms of self-association of a human    monoclonal antibody CNTO607. Protein engineering, design &    selection: PEDS 25, 531-7 (2012).-   5. Teplyakov, A. et al. Epitope mapping of anti-interleukin-13    neutralizing antibody CNTO607. Journal of molecular biology 389,    115-23 (2009).

EQUIVALENTS

While several inventive embodiments have been described and illustratedherein, those of ordinary skill in the art will readily envision avariety of other means and/or structures for performing the functionand/or obtaining the results and/or one or more of the advantagesdescribed herein, and each of such variations and/or modifications isdeemed to be within the scope of the inventive embodiments describedherein. More generally, those skilled in the art will readily appreciatethat all parameters, dimensions, materials, and configurations describedherein are meant to be exemplary and that the actual parameters,dimensions, materials, and/or configurations will depend upon thespecific application or applications for which the inventive teachingsis/are used. Those skilled in the art will recognize, or be able toascertain using no more than routine experimentation, many equivalentsto the specific inventive embodiments described herein. It is,therefore, to be understood that the foregoing embodiments are presentedby way of example only and that, within the scope of the appended claimsand equivalents thereto, inventive embodiments may be practicedotherwise than as specifically described and claimed. Inventiveembodiments of the present disclosure are directed to each individualfeature, system, article, material, kit, and/or method described herein.In addition, any combination of two or more such features, systems,articles, materials, kits, and/or methods, if such features, systems,articles, materials, kits, and/or methods are not mutually inconsistent,is included within the inventive scope of the present disclosure.

All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

All references, patents and patent applications disclosed herein areincorporated by reference with respect to the subject matter for whicheach is cited, which in some cases may encompass the entirety of thedocument.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B”, when usedin conjunction with open-ended language such as “comprising” can refer,in one embodiment, to A only (optionally including elements other thanB); in another embodiment, to B only (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, “or” should beunderstood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, i.e., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of” or “exactly one of,” or, when usedin the claims, “consisting of,” will refer to the inclusion of exactlyone element of a number or list of elements. In general, the term “or”as used herein shall only be interpreted as indicating exclusivealternatives (i.e. “one or the other but not both”) when preceded byterms of exclusivity, such as “either,” “one of,” “only one of,” or“exactly one of.” “Consisting essentially of,” when used in the claims,shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

It should also be understood that, unless clearly indicated to thecontrary, in any methods claimed herein that include more than one stepor act, the order of the steps or acts of the method is not necessarilylimited to the order in which the steps or acts of the method arerecited.

In the claims, as well as in the specification above, all transitionalphrases such as “comprising,” “including,” “carrying,” “having,”“containing,” “involving,” “holding,” “composed of,” and the like are tobe understood to be open-ended, i.e., to mean including but not limitedto. Only the transitional phrases “consisting of” and “consistingessentially of” shall be closed or semi-closed transitional phrases,respectively, as set forth in the United States Patent Office Manual ofPatent Examining Procedures, Section 2111.03.

Various aspects of the present invention may be used alone, incombination, or in a variety of arrangements not specifically discussedin the embodiments described in the foregoing and are therefore notlimited in their application to the details and arrangement ofcomponents set forth in the foregoing description or illustrated in thedrawings. For example, aspects described in one embodiment may becombined in any manner with aspects described in other embodiments.

Also, embodiments of the invention may be implemented as one or moremethods, of which an example has been provided. The acts performed aspart of the method(s) may be ordered in any suitable way. Accordingly,embodiments may be constructed in which acts are performed in an orderdifferent than illustrated, which may include performing some actssimultaneously, even though shown as sequential acts in illustrativeembodiments.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed. Such terms areused merely as labels to distinguish one claim element having a certainname from another element having a same name (but for use of the ordinalterm).

Having described several embodiments of the invention in detail, variousmodifications and improvements will readily occur to those skilled inthe art. Such modifications and improvements are intended to be withinthe spirit and scope of the invention.

What is claimed is:
 1. A method for producing a low-viscosity antibodysolution, the method comprising: (a) identifying surface-exposed aminoacid residues in tertiary structures of a plurality of antibodies bycomputing total solvent accessible area (SAA) values for amino acidresidues of the antibodies based on partial charge values assigned toside-chain atoms of the antibodies, wherein an amino acid residue isidentified as a surface-exposed amino acid residue if it has a SAA ofgreater than a threshold value; (b) identifying atoms that are within adistance R and belong to a side-chain of an exposed amino acid residuesidentified in (a); (c) computing an atomic spatial charge map (SCM)value for each atom identified in (b), wherein the atomic SCM value isthe sum of partial charges of the atoms identified in (b); (d) computinga SCM score for each of the antibodies based on the atomic SCM valuescomputed in (c); and (e) predicting relative viscosity of each of theantibodies based on the SCM score computed in (d) by comparing the SCMscores computed in (d) to each other, wherein an antibody is predictedto have a low viscosity if the SCM score computed in (d) is lower thanthe SCM scores of the other antibodies of the plurality; and (f)producing a solution comprising the antibody predicted to have a lowviscosity.
 2. The method of claim 1, wherein the tertiary structure ofthe antibodies is a Fab domain or a Fv domain of the antibody.
 3. Themethod of claim 1, wherein the partial charge in step (a) is assignedusing a software tool selected from CHARMM force field, PropKa,Accelrys, and Schrodinger.
 4. The method of claim 1, wherein thethreshold value is 1-50 Å².
 5. The method of claim 1, wherein thedistance R is 5-20 Å.
 6. The method of claim 4, wherein the thresholdvalue is 10 Å².
 7. The method of claim 5, wherein the distance R is 10Å.
 8. The method of claim 1, wherein the atomic SCM value in (c) iscomputed based on a pH value of greater than or equal to
 7. 9. Themethod of claim 1, wherein the atomic SCM value in (c) is computed basedon a pH value of less than or equal to 5.5.
 10. The method of claim 1,wherein the antibody is a monoclonal antibody.
 11. The method of claim1, wherein the antibody is an antibody fragment.
 12. The method of claim11, wherein the antibody fragment is a Fv, Fab, Fab′ or F(ab′)2fragment.